Corner-Case Image Generation With Guided Misclassification Handling
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Solution Overview
Problem
Traditional image generation methods are inefficient and produce images with significant quality issues, making it difficult to ensure adequate user experience for image generation requirements.
Innovation Solution
A method involving an image generation system that includes training an image generator with a corner case image set and additional guidance to generate new images, using a hybrid convolutional neural network architecture and semantic alignment techniques to enhance image quality and flexibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional image generation methods are used, then the process is simple, but the generation efficiency is low and image quality is poor
Solution Approach 1:
The patent applies preliminary action by pre-processing images to identify corner cases before the main generation process. The system detects images that are likely to be misclassified and prepares them for specialized processing, which improves both generation efficiency and quality by addressing problematic cases in advance
Solution Approach 2:
The patent implements local quality by applying different processing strategies to different types of images. Corner case images receive specialized attention with additional guidance and re-processing, while normal images follow the standard generation pipeline, optimizing both quality and efficiency for each category
2Manufacturing precision
If corner case images are identified and processed separately, then image quality improves, but the process complexity increases
Solution Approach 1:
The system applies self-service by using the image generator itself to identify corner cases through initial generation attempts. The same system that generates images also detects its own failures and triggers re-processing, eliminating the need for separate complex detection mechanisms
Solution Approach 2:
The patent implements feedback by using classification results to guide subsequent processing. Images that are misclassified or fall into corner cases are fed back into the generation process with additional guidance, creating a closed-loop system that continuously improves quality without requiring external complex control
3Manufacturing precision
If multiple processing passes are applied to corner case images, then generation quality improves, but processing time increases
Solution Approach 1:
The patent applies partial action by performing additional processing passes only on corner case images that require improvement, rather than applying multiple passes to all images. This selective approach maintains high quality for problematic cases while avoiding unnecessary time consumption for already-sufficient images
Data Source
AI summary
A method in an illustrative embodiment includes: acquiring an image set, where the image set includes a first plurality of images that can be classified into at least two categories; determining a corner case image set in the image set, where the corner case image set includes a second plurality of images that tend to be incorrectly classified; training an image generator with at least some images in the second plurality of images and first guidance associated with the at least some images; and generating an additional image by the trained image generator with a first image in the second plurality of images and additional guidance, where the additional guidance is different from the first guidance. By means of the technical solutions of the present disclosure, an image generation efficiency can be improved, and the quality of generated images can be enhanced, thereby improving user experience.